Structural optimization system, structural optimization methodlogy, and structural optimization program

ABSTRACT

The present invention enables a shape of an optimum structure to be clearly represented, and structural optimization having a high degree of freedom such as the allowance of a change in topology in a material domain Ω to be performed. The present invention sets a level set function φ; under a predetermined constraint condition, updates the level set function φ so as to bring a performance of a structure, such as a rigidity, close to a target value; moves a boundary ∂Ω between the material domain Ω and a void domain; allows a change in topology in the material domain Ω, which is associated with the update of the level set function φ, to form a new void domain in the material domain Ω; and moves a boundary ∂Ω between the new void domain and the material domain Ω.

TECHNICAL FIELD

The present invention relates to a structural optimization device, a structural optimization method, and a structural optimization program.

BACKGROUND ART

Conventionally, methods for structural optimization include dimensional optimization, shape optimization, and topology optimization.

The shape optimization among them is a method that obtains an optimum structure by using an outer shape as a design variable and updating the outer shape on the basis of sensitivity information, and is widely used in mechanical industries as a practical method.

However, the shape optimization has a disadvantage of making it difficult to change a configuration such as the number of holes in the optimum structure, and therefore significant improvement of structural performance cannot be expected.

On the other hand, the topology optimization enables a configuration of an optimum structure to be changed by replacing an optimal design problem by a material distribution problem to solve it, and therefore the significant improvement of a structural performance can be expected.

However, the topology optimization may give rise to a numerical instability problem such as a gray scale.

Also, in recent years, as a new structural optimization method, structural optimization based on a level set method, is proposed (Non-patent literature 1).

In this method, an outer shape is represented by a one-dimensional high-level level set function, and a change in shape and configuration is replaced by a change in the level set function value to obtain an optimum structure. On the basis of this, this method has advantages of, differently from the conventional topology optimization, being able to constantly clearly represent an outline of the optimum structure, and prevent a problem such as the gray scale from occurring.

However, the level set method updates the level set function on the basis of an advection equation, and is therefore based on the assumption that a change in topology (change in configuration) such as the introduction of a hole in the outer shape (material domain) is not allowed.

On the other hand, as described in Non-patent literature 2, there is proposed a method that, in the process of the structural optimization based on the level set method, on the basis of a topological derivative of an objective function, appropriately introduces a hole in an arbitrary manner. Also, as described in Non-patent literature 3, there is proposed a method that, in the process of optimization, by replacing a material domain (domain where a structure is formed) having a value of a topological derivative equal to a predetermined threshold value by a void domain (domain where a void is formed), allows the change in topology (change in configuration) such as the introduction of a hole in the outer shape (material domain).

However, any of these methods has a problem of being highly dependent on a parameter such as the number of holes or setting of the threshold value, and unless such parameter is appropriately set, these methods are unable to obtain a physically valid optimum structure (see Non-patent literature 4).

CITATION LIST Non Patent Literature

Non-patent literature 1: Wang, M. Y., Wang, X., and Guo, D., A Level Set Method for Structural Topology Optimization, Computer Methods in Applied Mechanics and Engineering, Vol. 192, (2003), pp. 227-246.

Non-patent literature 2: Yamasaki, S., Nishiwaki, S., Yamada, T., Izui, K., and Yoshimura, M., A Structural Optimization Method Based on the Level Set Method Using A New Geometry-based Re-initialization Scheme, International Journal for Numerical Methods in Engineering, Vol. 18, (2008), pp. 487-505.

Non-patent literature 3: Park, K. S., and Youn, S. K., Topology Optimization of shell structures using adaptive inner-front (AIF) level set method, Structural and Multidisciplinary Optimization, Vol. 36, (2008), pp. 43-58.

Non-patent literature 4: Yamada, T., Nishiwaki, S., Izui, K., and Yoshimura, M., A Study of Boundary Setting in the Design Domain for Structural Optimization Based on the Level Set Method, Transaction of the Japan Society for Industrial and Applied Mathematics, (submitted).

SUMMARY OF INVENTION Technical Problem

Therefore, the present invention is made to solve the above problems at once, and has a main desired object to enable a shape of an optimum structure to be clearly represented, and structural optimization having a high degree of freedom such as the allowance of a change in topology in a material domain to be performed.

Solution to Problem

That is, a structural optimization device of the present invention is characterized by being provided with the following configurations (1) to (3):

(1) A design domain data storage part that stores design domain data indicating a design domain for a structure;

(2) A level set function data storage part that stores level set function data that indicates a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and

(3) A level set function update part that, under a predetermined constraint condition, updates the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, moves the boundary between the material domain and the void domain, allows a change in topology (change in configuration) in the material domain to form a new void domain in the material domain, the change in topology being associated with the update of the level set function, and moves a boundary between the new void domain and the material domain.

If so, the predetermined value between the value representing the material domain and the value representing the void domain uses the level set function indicating the boundary between the material domain and the void domain to be thereby able to clearly represent a shape of an optimum structure. Also, the level set function is updated so as to move the boundary between the material domain and the void domain, and also move the boundary that is newly generated by allowing the change in topology in the material domain, and thereby structural optimization having a high degree of freedom such as the allowance of the change in topology in the material domain can be performed.

As a specific aspect for carrying out a method for updating the level set function, preferably, the level set function update part calculates, from an energy functional indicated by a function family using the level set function as a variable, an energy density in the material domain, an energy density in the void domain, and an interface energy density, and, according to an energy functional minimization principle, calculates a reaction-diffusion equation indicating time evolution of the level set function, and uses the reaction-diffusion equation to evolve in time the level set function, and thereby updates the level set function.

Also, preferably, the level set function update part updates the level set function such that a degree of complexity indicating structural complexity of a structure obtained as a result of updating the level set function is made equal to a preset degree of complexity. If so, among countless local optimum solutions, an optimum structure having structural complexity (i.e., fineness) intended by a designer (user) can be created.

Also, a structural optimization method according to the present invention is provided with: a design domain setting step of setting a design domain for a structure; a level set function setting step of setting a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update step of, under a predetermined constraint condition, updating the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, moving the boundary between the material domain and the void domain, allowing a change in topology (change in configuration) in the material domain to form a new void domain in the material domain, the change in topology being associated with the update of the level set function, and moving a boundary between the new void domain and the material domain.

Further, a structural optimization program according to the present invention is characterized by instructing a computer to be provided with functions as: a design domain data storage part that stores design domain data indicating a design domain for a structure; a level set function data storage part that stores level set function data that indicates a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update part that, under a predetermined constraint condition, updates the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, moves the boundary between the material domain and the void domain, allows a change in topology (change in configuration) in the material domain to form a new void domain in the material domain, the change in topology being associated with the update of the level set function, and moves a boundary between the new void domain and the material domain.

Advantageous Effects of Invention

As described, according to the present invention, a shape of an optimum structure can be clearly represented, and structural optimization having a high degree of freedom such as the allowance of a change in topology in a material domain can be performed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a device configuration diagram of a structural optimization device according to the present embodiment.

FIG. 2 is a functional configuration diagram of the structural optimization device according to the same embodiment.

FIG. 3 is a flowchart illustrating an optimization algorithm of the same embodiment.

FIG. 4 is a diagram illustrating a material domain, a void domain, and a boundary in a level set method.

FIG. 5 is a diagram illustrating a design domain and a boundary condition in a design problem 1 and a design domain and a boundary condition in a design problem 2.

FIG. 6 is a diagram illustrating an initial structure, structures in the process of optimization, and an optimum structure for the case of K₁(φ)=1.

FIG. 6 is a diagram illustrating an initial structure, structures in the process of optimization, and an optimum structure for the case of K₂(φp)=exp(−φ²).

FIG. 8 is a diagram illustrating an initial structure, structures in the process of optimization, and an optimum structure in Case 1 for comparing initial structures.

FIG. 9 is a diagram illustrating an initial structure, structures in the process of optimization, and an optimum structure in Case 2 for comparing the initial structures.

FIG. 10 is a diagram illustrating the comparison of complexity degree coefficients in the design problem 1.

FIG. 11 is a diagram illustrating the comparison of complexity degree coefficients in the design problem 2.

FIG. 12 is a diagram illustrating a design domain and a boundary condition in an optimization problem for the case of adding a uniform cross section constriction.

FIG. 13 is a diagram illustrating optimum structures for the cases of not adding and adding the uniform cross section constriction.

FIG. 14 is a diagram illustrating a design domain and a boundary condition in a heat transfer problem.

FIG. 15 is a diagram illustrating an initial structure and complexity degree coefficient based optimum structures in the heat transfer problem.

FIG. 16 is a diagram illustrating a design domain and a boundary condition in an internal heat generation problem.

FIG. 17 is a diagram illustrating complexity degree coefficient based optimum structures in the internal heat generation problem.

DESCRIPTION OF EMBODIMENTS

Next, one embodiment of the present invention is described referring to the drawings. Here, FIG. 1 is a device configuration diagram of a structural optimization device 100 of the present embodiment, FIG. 2 is a functional configuration diagram of the structural optimization device 100, and FIG. 3 is a flowchart illustrating operation of the structural optimization device 100.

<Device Configuration>

The structural optimization device 100 according to the present embodiment is one that, within a preset design domain, creates a structure having desired performance under a predetermined constraint condition, and a general-purpose or dedicated computer that is provided with, as illustrated in FIG. 1, in addition to a CPU 101, a storage device 102 such as a volatile memory or an HDD, and has input means 103 such as a mouse and a keyboard, an input/output interface 104 for connecting output means 105 such as a display and a printer for outputting an analysis model or a calculation result, and the like.

Also, a predetermined program is installed in the storage device 102 to, on the basis of the program, collaboratively operate the CPU 101 and peripheral devices, and thereby the structural optimization device 100 fulfills functions as, as illustrated in the functional configuration diagram of FIG. 2, a design domain data storage part 1, a level set function data storage part 2, a boundary condition data storage part 3, an analysis data storage part 4, a level set function update part 5, a calculation result output part 6, and the like.

In the following, each of the parts is described.

The design domain data storage part 1 is a part that stores pieces of design domain data indicating a design domain for a structure (including information on a structural grid (mesh) that divides the design domain into elements). In addition, the pieces of design domain data are inputted by, for example, a user who uses the input means 103.

The level set function data storage part 2 is a part that stores pieces of level set function data indicating a level set function for identifying a structure in the design domain, such as an initial structure. The level set function refers to a function that indicates whether each part of the design domain where the initial structure is set corresponds to a material domain (material phase) formed with the structure and occupied by a material, a void domain (void phase) where a void is formed, or a boundary between the domains, in which a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary between the material domain and the void domain. In addition, the pieces of level set function data are inputted by, for example, the user who uses the input means 103.

The boundary condition data storage part 3 is a part that stores pieces of boundary condition data indicating boundary conditions of the design domain (hereinafter also referred to as a fixed design domain). The specific boundary conditions include, for example, a constraint condition of the design domain, external force (surface force) acting on the initial structure, such as a load, and the like. In addition, the pieces of boundary condition data are inputted by, for example, the user who uses the input means 103.

The analysis data storage part 4 stores pieces of analysis data including a constraint condition value necessary for a volume constraint used to obtain an optimum structure, and material constants necessary to analyze a displacement field, such as a Young's modulus value and a Poisson's ratio value. In addition, the pieces of analysis data are inputted by, for example, the user who uses the input means 103.

The level set function update part 5 is a part that, under a predetermined constraint condition, updates a level set function so as to bring performances of the structure, such as a rigidity and an eigenfrequency, close to target values; moves the boundary between the material domain and the void domain; also allows a change in topology (change in configuration) in the material domain, which is associated with the update of the level set function, to form a new void domain (hole) in the material domain; and moves a boundary between the new void domain and the material domain.

Specifically, the level set function update part 5 calculates, from an energy functional indicated by a function family using the level set function as a variable, an energy density in the material domain, an energy density in the void domain, and an interface energy density, and, according to an energy functional minimization principle, calculates a reaction-diffusion equation indicating time evolution of the level set function; uses the reaction-diffusion equation to evolve in time the level set function; and thereby updates the level set function. In addition, a specific function of the level set function update part 5 will be described later.

The calculation result output part 6 is a part that outputs a calculation result of the level set function updated by the level set function update part 5, i.e., a shape of the optimum structure, and in the present embodiment, the output is displayed on the display 104.

<Operation of Structural Optimization Device 100>

Next, the operation of the structural optimization device 100 configured as described is described referring to FIG. 3.

First, the user operates the input means 103 to input the pieces of design domain data, pieces of level set function data, pieces of boundary condition data, and pieces of analysis data. Note that the pieces of level set function data inputted by the user indicate an initial level set function indicating the initial structure.

The respective pieces of data inputted in this manner are received by a data reception part (not illustrated), and the pieces of design domain data, pieces of level set function data, pieces of boundary condition data, and pieces of analysis data are respectively stored in the design domain data storage part 1, the level set function data storage part 2, the boundary condition data storage part 3, and the analysis data storage part 4 (Step S1).

Then, the level set function update part 5 calculates an objective functional (functional for bringing the performances of the structure, such as a rigidity and an eigenfrequency, close to the target values) corresponding to the level set function and a constraint functional (functional indicating the constraint condition) with use of a finite element method.

Subsequently, the level set function update part 5 determines whether or not a value of the objective functional converges (Step S3); if the value converges, determines that an optimum solution is obtained, to complete the optimization; and outputs the level set function at the time to the calculation result output part 6. On the other hand, if the value does not converge, the finite element method is used to update the level set function (Step S4). At this time, the level set function update part 5 determines whether or not the constraint condition (e.g., volume constraint) is met (Step S5), and if the constraint condition is met after the update, the flow returns to Step S2 again. On the other hand, if the constraint condition is met, an after-mentioned volume correction method is used to correct the level set function (Step S6), and the flow returns to Step S2. Note that if a volume of the initial structure is far from the volume constraint, a volume correction is made with taking approximately 200 steps so as to decrease the volume.

In the following, a method according to the present embodiment is described in detail along with the function of the level set function update part 5.

<Structural Optimization Problem Based on Level Set Method>

In a fixed domain D (hereinafter referred to as a fixed design domain) where the presence of a domain Ω (hereinafter referred to as a material domain) occupied by a material is allowed, consider structural optimization of the material domain. A level set method introduces a scalar function φ(x) referred to as a level set function in the fixed design domain D. The level set method is a method that implicitly represents a material boundary by using a zero iso-surface of the level set function φ (see FIG. 4).

In the present embodiment, as will be described later, on the basis of the concept of a phase field method, the level set function φ is evolved in time, and therefore an upper limit and a lower limit are set for the level set function φ, and defined by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 1} \right\rbrack & \; \\ \left\{ \begin{matrix} {1 > {\varphi (x)} > 0} & {{if}\mspace{14mu} {\forall{x \in {\Omega \backslash {\partial\Omega}}}}} \\ {{\varphi (x)} = 0} & {{if}\mspace{14mu} {\forall{x \in {\partial\Omega}}}} \\ {0 > {\varphi (x)} < {- 1}} & {{if}\mspace{14mu} {\forall{x \in {D\backslash \Omega}}}} \end{matrix} \right. & (1) \end{matrix}$

Note that if, inside the material domain, the level set function takes a positive real value, the same shape is represented, and therefore providing the above constraint is allowed.

With use of shape representation based on the level set method, the structural optimization problem is defined by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 2} \right\rbrack & \; \\ {{\inf\limits_{\varphi}{F\left( {\Omega (\varphi)} \right)}} = {\int_{\Omega}{{f(x)}{\Omega}}}} & (2) \\ {{{subject}\mspace{14mu} {to}\mspace{14mu} {G\left( {\Omega (\varphi)} \right)}} = {{{\int_{\Omega}{\Omega}} - V_{\max}} \leq 0}} & (3) \end{matrix}$

Here, F represents an objective functional, G represents a constraint functional, and V_(max) represents an allowable upper limit of a volume. Also, the objective functional F is an energy functional, and is represented by a free energy density in the material domain, free energy density in the void domain, and interface energy density. Further, the free energy density is given by a function family Ω(φ) using the level set function as a variable. This is because a phase of each point is identified by a value of the level set function φ, which gives the free energy density. That is, the free energy density is not an explicit function of the level set function φ, but an explicit function of a material shape Ω. Also, this is because, considering the mapping from the level set function φ to the material shape Ω, it is not uniquely determined.

In the topology optimization, a porous structure in which microvoids are dispersed everywhere, a plate structure in which ribs are arranged at extremely short intervals, or a generalized structure such as a mixture are allowed as an optimum design solution. For this reason, it is impossible to represent a structure smaller than a size of spatial discretization in numerical calculation, and therefore a degree of complexity indicating structural complexity of a structure is preset. Alternatively, the optimization problem should be relieved. From a practical perspective, it is preferable to eliminate an infinitely micro structure, and arbitrarily set the degree of complexity of a structure.

In a method for setting the degree of complexity, on the basis of the concept of the phase field method, and minimizing the energy functional taking into account the boundary energy, the degree of complexity (degree of fineness) of a structure is implicitly taken into account. That is, a diffusion term of a boundary energy term has a function of eliminating a microstructure to thereby set the degree of complexity of a structure. In the present embodiment, boundary energy representation of the phase field method is focused to implicitly set the degree of complexity of a structure. That is, there is proposed a method for solving the above-described problem by replacing the structural optimization problem given by equations (2) and (3) by an optimization problem of minimizing a sum of the boundary energy and objective functional given by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 3} \right\rbrack & \; \\ {{\inf\limits_{\varphi}{F\left( {{\Omega (\varphi)},\varphi} \right)}} = {{\int_{\Omega}{{f(x)}{\Omega}}} + {\int_{\Omega}{\frac{1}{2}\tau {{\nabla\varphi}}^{2}{\Omega}}}}} & (4) \\ {{{subject}\mspace{14mu} {to}\mspace{14mu} {G\left( {\Omega (\varphi)} \right)}} = {{{\int_{\Omega}{\Omega}} - V_{\max}} \leq 0}} & (5) \end{matrix}$

Here, a second term of the objective functional F represents the boundary energy. The level set function φ has the same profile as that of a phase field variable of the phase field method to thereby represent the boundary energy, and therefore in equation (1), the upper and lower limits of the level set function φ are set. Also, τ represents a parameter that gives a ratio between the boundary energy and the objective functional F, and is referred to as a complexity degree coefficient.

<Reaction-Diffusion Equation>

It is difficult to directly obtain the optimum solution φ of the optimization problem given by equations (4) and (5), and therefore the optimization problem is reduced to a problem to solve a time evolution equation with respect to the level set function φ. Typically, on the basis of outer shape sensitivity, according to an advection equation given by the following expression, the level set function φ is evolved in time, and thereby shape optimization is performed:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 4} \right\rbrack & \; \\ {{\frac{\partial\varphi}{\partial t} + {V_{N}{{\nabla\varphi}}}} = 0} & (6) \end{matrix}$

Here, V_(N) represents a normal component of an advection velocity of the material boundary, and the outer shape sensitivity obtained by formulating the optimization problem is replaced by this. This method is basically a method for shape optimization, and therefore has a problem that a change in topology such as the introduction of a void domain in a material domain is not allowed. For this reason, a method for introducing a hole in a material domain in an arbitrary manner is proposed; however, the method largely depends on parameter setting, and is difficult to stably obtain an optimum solution. Further, in a coupled problem of heat and structure, or in a coupled problem of static electric field, heat, and structure, it is known from many examination results that the method is extremely numerically unstable and poor in converging a solution. For this reason, it is desirable to reduce the optimization problem to the time evolution equation that ensures smoothness of the level set function φ and allows the change in topology such as the introduction of a hole in a material domain.

Therefore, in the present embodiment, as a method for solving the above-described problem, the concept of the phase field method that represents interface advection on the basis of interface diffusion is focused on, and on the basis of the same concept as that of the phase field method, the reaction-diffusion equation; that is, the time evolution equation with respect to the level set function φ is derived according to the energy functional minimization principle. That is, as given by the following expression, a driving force that evolves in time, the level set function φ is defined as one proportional to a gradient of the objective functional F:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 5} \right\rbrack & \; \\ {\frac{\partial\varphi}{\partial t} = {{- {K(\varphi)}}\frac{\delta \; F}{\delta\varphi}}} & (7) \end{matrix}$

Here, K(φ)>0 represents a proportionality constant, and δF/δφ represents a functional derivation of the objective functional F. By substituting equation (4) into the above expression, the following expression is obtained:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 6} \right\rbrack & \; \\ {\frac{\partial\varphi}{\partial t} = {{- {K(\varphi)}}\left( {{{f(x)}\frac{\Omega}{\varphi}{H(\varphi)}} - {\tau {\nabla^{2}\varphi}}} \right)}} & (8) \end{matrix}$

Here, H(φ) represents a Heaviside function. Also, it turns out that the sensitivity dΩ/dφ of the level set function φ with respect to the shape Ω(φ) can be considered as a constant C if the level set function φ is made constant in the material domain Ω. Accordingly, the above expression can be replaced by the following expression:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 7} \right\rbrack & \; \\ {\frac{\partial\varphi}{\partial t} = {{- {K(\varphi)}}\left( {{{{Cf}(x)}{H(\varphi)}} - {\tau {\nabla^{2}\varphi}}} \right)}} & (9) \end{matrix}$

The proportionality constant K and the complexity degree coefficient τ are parameters, so that the constant C can be treated as a parameter, and is only required to be set so as to make the profile of the level set function φ steep. Also, regarding the boundary conditions, at a boundary ∂D_(N) (hereinafter referred to as a non-design boundary) that is preliminarily specified as a material domain boundary, a Dirichlet boundary condition is given, whereas at the other boundaries, a Neumann boundary condition is given, and thereby the absence of influence of an outside of the fixed design domain D is represented. In this case, the time evolution equation is given by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 8} \right\rbrack & \; \\ \left\{ \begin{matrix} {\frac{\partial\varphi}{\partial t} = {{- {K(\varphi)}}\left( {{C{\overset{\_}{f}(x)}{H(\varphi)}} - {\tau {\nabla^{2}\varphi}}} \right)}} \\ {\frac{\partial\varphi}{\partial n} = {0\mspace{14mu} {on}{\mspace{11mu} \;}\frac{\partial D}{\partial D_{N}}}} \\ {\varphi = {1\mspace{14mu} {on}\mspace{14mu} {\partial D_{N}}}} \end{matrix} \right. & (10) \end{matrix}$

In addition, the phase field method is proportional to a gradient of domain integration for the case of using a function of the phase field variable as an integrand, whereas the present embodiment is different in that the present embodiment is proportional to a gradient due to a domain variation and evolves in time with the level set function φ. As a result, in the phase filed method, in the process of time evolution, an interface is only advected, whereas according to the method proposed in the present embodiment, the change in topology such as the creation of the void domain in the material domain is allowed. Also, note that the advection equation (6) used in the conventional method does not ensure the smoothness of the level set function φ, whereas the time evolution equation (10) is a partial differential equation referred to as the reaction-diffusion equation, and includes the diffusion term, and thereby the smoothness of the level set function φ is ensured.

Regarding the volume constraint, a method that imposes the volume constraint according to the Lagrange's method of undetermined multipliers is used. In this case, given that a Lagrange multiplier is denoted by λ, the time evolution equation (9) is given by the following expression:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 9} \right\rbrack & \; \\ \begin{matrix} {\frac{\partial\varphi}{\partial t} = {{- {K(\varphi)}}\left( {{{C\left( {{f(x)} + \lambda} \right)}{H(\varphi)}} - {\tau {\nabla^{2}\varphi}}} \right)}} \\ {:={{- {K(\varphi)}}\left( {{C{\overset{\_}{f}(x)}{H(\varphi)}} - {\tau {\nabla^{2}\varphi}}} \right)}} \end{matrix} & (11) \end{matrix}$

The Lagrange multiplier λ is, if the constraint condition is active, given by:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 10} \right\rbrack & \; \\ \begin{matrix} {\frac{{G(\varphi)}}{t} = {\int_{\Omega}^{\;}{\frac{\partial\varphi}{\partial t}\ {\Omega}}}} \\ {= 0} \end{matrix} & (12) \end{matrix}$

Accordingly, from equations (11) and (12), the Lagrange multiplier λ is given by the following expression:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 11} \right\rbrack & \; \\ {\lambda = {- \frac{\int_{\Omega}^{\;}{{K(\varphi)}\left( {{f(x)} + {\tau {\nabla^{2}\varphi}}} \right)\ {\Omega}}}{\int_{\Omega \;}^{\;}{{K(\varphi)}\ {\Omega}}}}} & (13) \end{matrix}$

Also, if the constraint condition is inactive, it is given by the following expression:

[Expression 12]

λ=0   (14)

From the above, a value of the Lagrange multiplier λ is obtained to update the level set function φ from an initial value on the basis of equation (10), and the level set function φ at the time when the objective functional F converges gives the optimum structure.

Note that it can be checked from the following expression that in the case of evolving in time the level set function φ is evolved on the basis of equation (10), the objective functional F monotonically decreases:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 13} \right\rbrack & \; \\ \begin{matrix} {\frac{F}{t} = {\int_{D}^{\;}{\frac{\delta \; F}{\delta\varphi}\frac{\partial\varphi}{\partial t}\ {D}}}} \\ {= {\int_{D}^{\;}{\frac{\delta \; F}{\delta\varphi}\left( {{- {K(\varphi)}}\frac{\delta \; F}{\delta\varphi}} \right){{D\left( {\because(7)} \right)}}}}} \\ {= {{\int_{D}^{\;}{{K(\varphi)}\ \left( \frac{\delta \; F}{\delta\varphi} \right)^{2}{D}}} < 0}} \end{matrix} & (15) \end{matrix}$

In addition, in the phase field method, the energy density is defined as a function with respect to a discriminant function, and therefore the boundary ∂D is advected to thereby monotonically decrease the energy functional. For this reason, in the process of time evolution, the change in topology such as the introduction of the void domain in the material domain is not allowed. On the other hand, according to the method of the present embodiment, by introducing the function family Ω(φ), the representation of a material shape is made proportional to the gradient with respect to a variation of the material shape, and the level set function φ is evolved in time. Note that as a result, in addition to the movement of the boundary ∂D, the change in topology, which is not arbitrary but automatic, is allowed.

<Mean Compliance Minimization Problem>

Consider a structural problem of, with respect to the fixed design domain D where a mixture of the material domain filled with a linear elastic material and the void domain is allowed, completely constraining a boundary Γ_(u), and acting a surface force t on a boundary Γ_(t) and a material force b on the material domain. Note that the boundary Γ_(u) is assumed to be fixed to the fixed design domain boundary ∂D. In this case, under the volume constraint, the structural optimization problem of minimizing a mean compliance is described as the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 14} \right\rbrack & \; \\ {{\inf\limits_{\Omega}{F(\Omega)}} = {l(u)}} & (16) \\ {{{subject}\mspace{14mu} {to}\mspace{14mu} {a\left( {u,v} \right)}} = {{l(v)}\mspace{14mu} {for}\mspace{14mu} {\forall{v \in {U\mspace{14mu} u} \in U}}}} & (17) \\ {{G(\Omega)} \leq 0} & (18) \end{matrix}$

Here, respective notations in the above expressions are defined by the following expressions:

[Expression 15]

a(u, v)=∫_(Ω) e(u):E:e(v)dΩ  (19)

l(v)=∫_(Ω) t·vdΓ+∫ _(Ω) b·vdΩ  (20)

G(Ω)=∫_(Ω) dΩ−V _(max)   (21)

Further, ε represents a strain tensor, F represents an elasticity tensor, V_(max) represents a constraint value of a volume, and U represents a displacement function space defined by the following expression:

[Expression 16]

U={v=v _(i) e _(i) :v _(i) ∈H ^(l)(D) with v=0 on T _(t)}  (22)

Note that the boundary Γ_(t) on which the surface force acts is constantly required to be the material boundary, and therefore assumed to be the non-design boundary δD_(N).

Next, KKT conditions for the above optimization problem are derived to give, from a result of the derivation, a function f(x) necessary to update the level set function φ. On the basis of the above formulation, the Lagrangian F′(Ω) is described with use of the Lagrange multiplier λ and an adjoint displacement field v as follows:

[Expression 17]

F′(Ω)=l(u)+a(u,v)=l(v)+λG(Ω)   (23)

If equation (23) is used to derive the KKT conditions, the following expressions are obtained:

[Expression 18]

δF′(Ω)=0, a(u,v)−l(v)=0,

λG(Ω)=0, λ≧0, G(Ω)≦0   (24)

The level set function φ meeting the KKT conditions serves as a candidate for the optimum solution (optimum structure). However, it is difficult to directly obtain the level set function φ meeting these conditions, and therefore by giving a level set function φ serving as an appropriate initial structure and using equation (11) to update the level set function φ, a sum of the Lagrangian F′(Ω) and the boundary energy is decreased.

In the following, a function f′(x) that is necessary to use equation (11) to update the level set function φ and gives an objective functional of the Lagrangian is derived. Here, by defining the adjoint displacement field v as the following expression, the optimization problem can be replaced by a self-adjoint problem.

[Expression 19]

a(v,u)=l(u) for ∀u∈U v∈U   (25)

By substituting equation (25) into equation (23) and using the equilibrium equations (24), the Lagrangian F′(Ω) is given by the following expression:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 20} \right\rbrack & \; \\ \begin{matrix} {{F^{\prime}(\Omega)} = {{l(u)} + {\lambda \; {G(\Omega)}}}} \\ {= {{a\left( {u,u} \right)} + {\lambda \; {G(\Omega)}}}} \\ {= {\int_{\Omega}^{\;}{\left( {{ɛ(u)}:{E:{{ɛ(u)} + \lambda}}} \right)\ {\Omega}}}} \end{matrix} & (26) \end{matrix}$

Accordingly, the integrand f′(x) that gives the objective functional is given by the following expression:

[Expression 21]

f′(x)=ε(u):E:ε(u)+λ  (27)

<Numerical Solution of Reaction-Diffusion Equation>

As given by the following expressions, equation (10) is discretized in a time direction by a finite difference method:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 22} \right\rbrack & \; \\ \left\{ \begin{matrix} {{\frac{\varphi \left( {t + {\Delta \; t}} \right)}{\Delta \; t} - {{K\left( {\varphi (t)} \right)}\tau {\nabla^{2}{\varphi \left( {t + {\Delta \; t}} \right)}}}} =} \\ {{{- {K\left( {\varphi (t)} \right)}}{{Cf}^{\prime}\left( {x,t} \right)}{H\left( {\varphi,t} \right)}} + \frac{\varphi (t)}{\Delta \; t}} \\ {\varphi = {1\mspace{14mu} {on}\mspace{14mu} {\partial D_{N}}}} \\ {\frac{\partial\varphi}{\partial n} = {0\mspace{14mu} {on}\mspace{14mu} \frac{\partial D}{\partial D_{N}}}} \end{matrix} \right. & (28) \end{matrix}$

Here, Δt represents a time increment. Note that by replacing the diffusion term τ∇²φ of the first expression of equation (15) by an updated value, a diffusion effect due to the boundary energy can be considered for the updated level set function φ. Accordingly, in all update steps, the diffusion effect is taken into account.

Next, in order to use the finite element method to perform discretization in a space direction, a weak form of equation (28) is derived, which results in the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 23} \right\rbrack & \; \\ \left\{ \begin{matrix} {{\int_{D}^{\;}{\frac{\varphi \left( {t + {\Delta \; t}} \right)}{\Delta \; t}\overset{\sim}{\varphi}{D}}} + {\int_{D}^{\;}{{\nabla^{T}{\varphi \left( {t + {\Delta \; t}} \right)}}\left( {\tau \; {K\left( {\varphi (t)} \right)}{\nabla\overset{\sim}{\varphi}}} \right)\ {D}}}} \\ {= {\int_{D}^{\;}{\left( {{{- {K\left( {\varphi (t)} \right)}}{{Cf}^{\prime}\left( {x,t} \right)}{H\left( {\varphi,t} \right)}} + \frac{\varphi (t)}{\Delta \; t}} \right)\overset{\sim}{\varphi}\ {D}}}} \\ {{for}\mspace{14mu} {\forall{\overset{\sim}{\varphi} \in \overset{\sim}{\varphi}}}} \\ {\varphi = {1\mspace{14mu} {on}\mspace{14mu} {\partial D_{N}}}} \end{matrix} \right. & (29) \end{matrix}$

Here, φ^(˜) is defined by the following expression, and represents a functional space with respect to the level set function φ:

[Expression 24]

{tilde over (Φ)}={φ∈ H ¹(D) with φ=1 on ∂D _(N)}  (35)

By using the finite element method to discretized equation (29), the following expressions are obtained:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 25} \right\rbrack & \; \\ \left\{ \begin{matrix} {{T\; {\Phi \left( {t + {\Delta \; i}} \right)}} = Y} \\ {\varphi = {1\mspace{14mu} {on}\mspace{14mu} {\partial D_{N}}}} \end{matrix} \right. & (36) \end{matrix}$

Here, Φ(t) is a vector formed by a level set function value at each nodal point at time t, and the matrix T and the vector Y are respectively given by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 26} \right\rbrack & \; \\ {T = {\bigcup\limits_{e}{\int_{V_{e}}^{\;}{\left( {{\frac{1}{\Delta \; t}N^{T}N} + {{\nabla^{T}{{NK}\left( {\varphi (t)} \right)}}\tau {\nabla\; N}}} \right)\ {V_{e}}}}}} & (37) \\ {Y = {\bigcup\limits_{e}{\int_{V_{e}}^{\;}{\left( {{{- {K\left( {\varphi (t)} \right)}}{s\left( {x,t} \right)}} + \frac{\varphi \left( {x,t} \right)}{\Delta \; t}} \right)N\ {V_{e}}}}}} & (38) \end{matrix}$

In the present embodiment, the level set function φ is updated on the basis of an implicit method, and therefore any limitation based on a CFL condition is not present in the time increment Δt. As a result, by increasing the time increment Δt, there can be avoided a problem that as the time t is increased, the boundary movement velocity gradually decreases. Note that within approximately first 20 update steps, the time increment Δt is set such that a maximum value of a variation of the level set function φ becomes equal to approximately 1, and then the time increment is gradually increased.

<Approximate Analytical Method for Displacement Field Based on Eulerian Coordinate System>

In order to update the level set function φ, the displacement field should be analyzed in the material domain. In the case of analyzing the material domain on the basis of a Lagrangian coordinate system, there occur problems that a material shape is different for each of update steps, so that a mesh should be sequentially created, and a discretely obtained displacement field should be mapped into the fixed design domain D. Further, in the process of optimization, if a material domain not connected to a displacement fixation part appears, there occurs a problem that the material domain has a rigid body mode. For these reasons, in the present embodiment, on the basis of an Eulerian coordinate system, the void domain is regarded as a material having a small longitudinal elastic modulus, whereas near the boundary, on the assumption that a longitudinal elastic modulus spatially smoothly changes, approximation is performed. That is, by replacing the equilibrium equation (24) by the following expression, the displacement field is analyzed without explicitly extracting a material shape:

[Expression 27]

∫_(Ω)ε(u):E:ε(v)H _(e)(φ)dΩ=∫ _(Ω) b·vH _(e)(φ)dΩ+∫ _(Ω) t·vdΓ  (39)

Here, H_(e)(φ) is a function described by the following expression:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 28} \right\rbrack & \; \\ {{H_{e}(\varphi)} = \left\{ \begin{matrix} d & \left( {\varphi < {- t}} \right) \\ {\frac{1 + d}{2} + {\left( {{\frac{15}{16}\varphi} - {\frac{5}{8}\varphi^{3}} + {\frac{3}{16}\varphi^{5}}} \right)\left( {1 - d} \right)}} & \left( {{- t} < \varphi < t} \right) \\ 1 & \left( {\varphi > t} \right) \end{matrix} \right.} & (40) \end{matrix}$

Here, d represents a relative value (ratio) of the longitudinal elastic modulus of the void domain to the longitudinal elastic modulus of the material domain, and t represents a value indicating a transition width of a material constant, both of which are set to be sufficiently small values. Further, the volume constraint function G(Ω) is also obtained with use of the following approximation:

[Expression 29]

G(φ)=∫_(D) H _(e)(φ)dΩ−V _(max)   (41)

<Details of Implementation Method>

The parameter K(φ) is a function with respect to the level set function φ, and therefore it turns out that the parameter K(φ) is a parameter (mobility) that determines a degree of priority between the movement of the boundary ∂D and the change in structural configuration. That is, setting K(φ) near the boundary to be large corresponds to giving priority to the movement of the boundary ∂D over the change in topology (change in configuration). It turns out from many calculation results that independently of the setting of the parameter K(φ), the same optimum structure is obtained. In regard to this point, in the case of K₁(φ)=1 (constant), and in the case of a Gaussian distribution around φ=0, i.e., K₂(φ)=exp(−φ²), respective results are described in numerical examples. In addition, in the case of K₁(φ)=1, it is constant independently of φ, and therefore the degree of priority of the movement of the boundary ∂D and that of the change in topology are comparable, whereas in the case of K₂(φ), a value near φ=0 is large, and therefore priority is given to the movement of the boundary ∂D.

Also, in the case of taking into account the volume constraint on the basis of the Lagrange's method of undetermined multipliers, a numerical error with respect to the volume constraint in each of the update steps is minute; however, by performing iterative calculation, there occurs a problem that the numerical error is accumulated. For this reason, in the case where the volume constraint is not met, by making a correction to the level set function φ, a volume correction should be made. In the present embodiment, a minimum value of a minute value Δφ(x) meeting the following expression is obtained by using a bisection method, and φ(x)+Δφ(x) is newly set to be a value of the level set function φ:

[Expression 30]

G(φ(x)+Δφ(x))≦0   (42)

NUMERICAL EXAMPLES

Next, the structural optimization device 100 of the present embodiment is used to examine validity of the above-described method.

FIG. 5( a) illustrates the design domain D and the boundary condition in a design problem 1. The fixed design domain D is set to be a rectangular domain having a size of 8×10⁻² m×6×10⁻² m, and divided into elements by a structural grid having an element length of 5×10⁻⁴ m. Also, an allowable maximum volume value V_(max) is set to be 40% of the fixed design domain D. The boundary condition is set such that a left-hand side is completely constrained in displacement, and downward surface force is made to act on the center of a right hand side.

FIG. 5( a) illustrates the design domain D and the boundary condition in a design problem 2. The fixed design domain D is set to be a rectangular domain having a size of 8×10⁻² m×6×10⁻² m, and divided into elements by a structural grid having an element length of 5×10⁻⁴ m. Also, the allowable maximum volume value V_(max) is set to be 50% of the fixed design domain D. The boundary condition is set such that a left-hand part of a lower side and a right-hand part of the lower side are completely constrained in displacement, and downward surface force is made to act on the center of the lower side.

In any of the cases of the design problems 1 and 2, to analyze the displacement field, four-node isoparametric quadrilateral plane stress elements are used, and to update the level set function φ, the four-node isoparametric quadrilateral elements are used. As an analysis model material, in any of the cases, an isotropic material is assumed, and as material constants, the longitudinal elastic modulus and Poisson's ratio are respectively set to 210 GPa and 0.3. Further, the ratio of the longitudinal elastic modulus of the void domain to the longitudinal elastic modulus of the material domain, which is defined in equation (35), is set to 1×10⁻⁶, and the setting value t of the implicit transition width is set to 0.1.

<Comparison of Parameters K(φ)>

First, a comparison of the parameters K(φ) is made.

Under the conditions that an initial structure is set to be a structure in which upper ⅔ of the fixed design domain D are occupied, the complexity degree coefficient τ is set to 0.07, and C is set to be C=1/max(f(x)), the optimization is performed on the design problem 1.

The initial structure, structures in the process of the optimization, and the optimum structure for the case of K₁(φ)=1 are illustrated in FIG. 6. In the same manner, the initial structure, structures in the process of the optimization, and the optimum structure for the case of K₂(φ)=exp(−φ²) are illustrated in FIG. 7. In FIGS. 6 and 7, a black portion corresponds to the material domain, and a gray portion corresponds to the void domain. If a comparison between results in FIGS. 6 and 7 is made, it turns out that an appearance of the process of the optimization is different, and the result in FIG. 7 where the priority is given to the movement of the boundary ∂D exhibits an appearance where the boundary ∂D moves faster. Also, it turns out that the obtained optimum structures exhibit the same structure, and clear and physically valid results are obtained.

<Comparison of Initial Structures>

Next, different initial structures are set to perform the optimization on the design problem 1.

One (Case 1) of the initial structures is, as illustrated in FIG. 8( a), set to be a structure in which the entire domain of the fixed design domain D is occupied by the material domain, and the other (Case 2) initial structure is, as illustrated in FIG. 9( a), set to be a structure in which two holes are formed in the initial structure in Case 1. Also, in any of Case 1 and Case 2, the complexity degree coefficient τ is set to 0.07, and other parameters are set to be K₁(φ)=1 and C=1/max(f(x)). FIGS. 8 and 9( b) and (c) illustrate structures in the process of the optimization, and FIGS. 8 and 9( d) illustrate the optimum structures. It turns out that obtained results exhibit the same optimum structure, which is also a physically valid structure.

<Comparison of Complexity Degree Coefficients>

Next, a setting value of the complexity degree coefficient τ is varied to perform the optimization on the design problems 1 and 2.

In this comparison, the structure in which the entire domain of the fixed design domain D is occupied by the material domain is set to be the initial structure, and K₁(φ) and C are respectively set to be K₁(φ)=1 and C=1/max(f(x)). Also, in the design problem 1, regarding the complexity degree coefficient τ, setting values in Case 1, Case 2, and Case 3 are respectively set to 0.5, 0.05, and 0.03. On the other hand, in the design problem 2, the setting values in Case 1, Case 2, and Case 3 are respectively set to 0.01, 0.005, and 0.0001. In addition, in any of the design problems, a value of the complexity degree coefficient τ in Case 2 is smaller, and therefore a more complex (detailed) structure is allowed as the optimum solution.

FIG. 10 illustrates optimum structures in Case 1, Case 2, and Case 3 in the design problem 1, and FIG. 11 illustrates optimum structures in Case 1, Case 2, and Case 3 in the design problem 2. It turns out from these diagrams that in any of the cases, a physically valid optimum structure is obtained. Further, it turns out that depending on a magnitude of the complexity degree coefficient τ, the degree of complexity of an optimum structure is different. Accordingly, it can be checked that the method proposed in the present embodiment can implicitly set the degree of complexity of a structure by setting the complexity degree coefficient, and is a method that can qualitatively take into account the degree of complexity of an optimum structure.

<Optimization Added with Manufacturing Constraint (Uniform Cross Section Constraint)>

FIG. 12 illustrates the design domain and the boundary condition in the optimization for the case of providing a uniform cross section constraint. The fixed design domain D is set to be a rectangular parallelepiped domain having a size of 2.0 m×0.8 m×0.15 m, and underneath it, the non-design domain formed in a rectangular parallelepiped shape having a size of 2.0 m×0.8 m×0.05 m is set. The boundary condition is set such that both edges in a longer direction are completely constrained in displacement, and downward surface force is made to act on the entire lower surface of the non-design domain.

In this case, a geometric constraint is added such that the optimum structure has a uniform cross section in an X₃ direction. Specifically, the complexity degree coefficient τ is set to be anisotropic, and a complexity degree coefficient in a uniform cross section direction is set to be larger than the other complexity degree coefficients. That is, among the complexity degree coefficients τ(X₁), τ(X₂), and τ(X₃) in the respective axial directions, τ(X₃) is set to be sufficiently larger than τ(X₁) and τ(X₂), (τ(X₃)>>τ(X₁), τ(X₂)).

FIG. 13 illustrates the optimum structure for the case of (A) “without uniform cross section constraint” and the optimum structure for the case of (B) “with uniform cross section constraint”. It turns out from these diagrams that by making the complexity degree coefficient anisotropic (τ(X₃)>>τ(X₁), τ(X₂)), the optimum structure added with the uniform cross section constraint is obtained.

<Heat Diffusion Problem>

In the above comparative examples and the like, the case of the application to a rigidity maximization problem is described; however, the application to a heat diffusion problem is also possible. As the heat diffusion problem, a heat conduction problem, a heat transfer problem, or an internal heat generation problem is possible.

In the following, as specific examples, the heat transfer problem and the internal heat generation problem are described.

<Formulation of Heat Diffusion Maximization Problem>

With respect to the fixed design domain D including the material domain filled with a linear heat conductor and the void domain, consider a heat diffusion maximization problem in which a temperature provision of a temperature T₀ at a boundary Γ_(t), a heat transfer boundary having a heat transfer coefficient h at a boundary Γ_(h), a heat flux boundary having a heat flux q at a boundary Γ_(q), and an internal heat generation Q in the fixed design domain D are given. In addition, it is assumed that the boundaries Γ_(t) and Γ_(q) are set on the boundary ∂D of the fixed design domain D. Also, the boundary Γ_(h) is set on a boundary of the material domain inside the fixed design domain, and serves as a design variable dependent boundary condition that is determined depending on a value of the level set function, which serves as a design variable. In this case, the heat diffusion maximization problem is formulated as a total potential energy maximization problem given by the following expressions:

$\begin{matrix} \left\lbrack {{Expression}\mspace{14mu} 31} \right\rbrack & \; \\ {{\inf\limits_{\varphi}{F\left( {\Omega (\varphi)} \right)}} = {- \left( {{\frac{1}{2}{a\left( {u_{t},u_{t}} \right)}} - {l\left( u_{t} \right)}} \right)}} & (43) \\ {{{subject}\mspace{14mu} {to}\mspace{14mu} {a\left( {u_{t},v_{t}} \right)}} = {{l\left( u_{t} \right)}\mspace{14mu} {for}\mspace{14mu} {\forall{v_{t} \in {U\mspace{14mu} u_{t}} \in U_{t}}}}} & (44) \\ {{G\left( {\Omega (\varphi)} \right)} \leq 0} & (45) \end{matrix}$

Here, respective notations in the above expressions are defined by the following expressions:

[Expression 32]

a(u _(t) ,v _(t))=∫_(Ω(φ)) ∇u _(t) K ∇v _(t) dΩ  (46)

l(v _(t))=∫_(Cq) qv _(i) dT+∫ _(D) Qv _(f) dΩ+∫ _(Cq(φ)) h(u _(i) −T _(amp))v _(i) dT   (47)

G(Ω(φ))=∫_(Ω(φ)) dΩ−V _(max)   (48)

Further, K and T_(amp) respectively represent a heat conduction tensor and an ambient temperature, and U_(t) represents a temperature function space defined by the following expression:

[Expression 33]

U _(t) ={v _(t) ∈H ^(t)(D) with v _(t) =T _(Q) on T _(u)}  (48)

In addition, in equation (43), a total potential energy is added with a minus sign, and thereby formulated as a minimization problem of an objective function.

<Numerical Example of Heat Diffusion Problem (Heat Transfer Problem)>

FIG. 14 illustrates the design domain and the boundary condition. The fixed design domain is set to be a square domain having a size of 1×10⁻² m×1×10⁻² m, and divided into elements by a structural grid having an element length of 5.0×10⁻⁵ m. As illustrated in the diagram, a lower part of a left-hand side and a left-hand part of a lower side are set to have a temperature of T₀=50° C., and at the rest of the boundary, an adiabatic boundary is given. Also, inside the fixed design domain, the heat transfer boundary is given under the conditions of a heat transfer coefficient h=0.1 W/(m²K) and the ambient temperature T_(amp)=25° C. The allowable maximum volume value V_(max) is set to be 60% of the fixed design domain. Also, in order to define the heat transfer boundary, at least one boundary between the material domain and the void domain is required in the initial structure, and therefore as illustrated in FIG. 15( a), the initial structure is set to be a quarter of a circle around the lower left corner.

FIGS. 15( b) to (e) illustrate optimum structures obtained in the cases of setting the complexity degree coefficient τ to 5×10⁻⁴, 1×10⁻⁴, 5×10⁻⁵, and 1×10⁻⁵, respectively. It turns out from FIG. 15 that depending on a setting value of the complexity degree coefficient, complexity of a fin shape is varied. Also, it turns out that, in any of the cases, a physically valid optimum structure is obtained.

<Numerical Example of Heat Diffusion Problem (Internal Heat Generation Problem)>

FIG. 16 illustrates the design domain and the boundary condition. The fixed design domain is set to be a square domain having a size of 1×10⁻² m×1×10⁻² m, and divided into elements by a structural grid having an element length of 2.5×10⁻⁵ m. As illustrated in FIG. 16, a central part of an upper side is set to have a temperature of T₀=25° C., and at the rest of the boundary, an adiabatic boundary is given. Also, inside the fixed design domain, a heat amount Q=1.0×10⁻⁷ W/m³ is given, and the allowable maximum volume value V_(max) is set to be 40% of the fixed design domain. Also, the initial structure is set to be a structure in which the entire domain of the fixed design domain is filled with a material. In this case, in order to examine a relationship between the complexity degree coefficient and the optimum structure, the complexity degree coefficient is varied to perform the optimization.

FIGS. 17( a) to (d) illustrate optimum structures obtained in the cases of setting the complexity degree coefficient τ to 5×10⁻⁵, 1×10⁻⁵, 5×10⁻⁶, and 1×10⁻⁶, respectively. In addition, the design domain is bilaterally symmetric, and therefore optimization analysis is performed only on the right half. It turns out from these results that, even in this design problem, depending on the setting of the complexity degree coefficient, complexity of the optimum structure is varied. Also, it turns out that, in any of the cases, a physically valid optimum structure is obtained.

Effects of the Present Embodiment

According to the structural optimization device 100 configured as described according to the present embodiment, the predetermined value between the value representing the material domain Ω and the value representing the void domain uses the level set function φ indicating the boundary ∂D between the material domain Ω and the void domain, and thereby a shape of an optimum structure can be clearly represented. Also, the level set function φ is updated so as to move the boundary ∂D between the material domain Ω and the void domain, and also move the boundary ∂D that is newly generated by allowing the change in topology in the material domain Ω, and thereby structural optimization having a high degree of freedom such as the allowance of the change in topology in the material domain Ω can be performed.

Note that it should be appreciated that the present invention is not limited to the above embodiment, but various modifications can be made without departing from the scope of the present invention.

For example, the structural optimization device of the above embodiment is one that is applied to the rigidity maximization problem or heat diffusion maximization problem, but can be applied to various structural problems such as, in addition to it, an eigenfrequency maximization problem. Industrial applicability

By applying the present invention, a shape of an optimum structure can be clearly represented, and structural optimization having a high degree of freedom such as the allowance of a change in topology in a material domain Ω can be performed.

REFERENCE SIGNS LIST

100: Structural optimization device

D: Design domain

Ω: Material domain

∂Ω: Domain boundary

φ: Level set function

1: Design domain data storage part

2: Level set function data storage part

5: Level set function update part 

1. A structural optimization device comprising: a design domain data storage part that stores design domain data indicating a design domain for a structure; a level set function data storage part that stores level set function data that indicates a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the void and material domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update part that, under a predetermined constraint condition, updates the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, and simultaneously varies a configuration and a shape of the boundary between the material domain and the void domain.
 2. The structural optimization device according to claim 1, wherein the level set function update part updates the level set function such that a degree of complexity indicating structural complexity of a structure obtained as a result of updating the level set function is made equal to a preset degree of complexity.
 3. A structural optimization device comprising: a design domain data storage part that stores design domain data indicating a design domain for a structure; a level set function data storage part that stores level set function data that indicates a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the material and void domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update part that, under a predetermined constraint condition, updates the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, moves the boundary between the material domain and the void domain, allows a change in topology (change in configuration) in the material domain to form a new void domain in the material domain, the change in topology being associated with the update of the level set function, and moves a boundary between the new void domain and the material domain, wherein the level set function update part calculates, from an energy functional indicated by a function family using the level set function as a variable, an energy density in the material domain, an energy density in the void domain, and an interface energy density, and, according to an energy functional minimization principle, calculates a reaction-diffusion equation indicating time evolution of the level set function, uses the reaction-diffusion equation to evolve in time the level set function, and thereby updates the level set function.
 4. A structural optimization method comprising: a design domain setting step of setting a design domain for a structure; a level set function setting step of setting a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update step of, under a predetermined constraint condition, updating the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, and simultaneously varying a configuration and a shape of the boundary between the material domain and the void domain.
 5. A structural optimization program that instructs a computer to comprise functions as: a design domain data storage part that stores design domain data indicating a design domain for a structure; a level set function data storage part that stores level set function data that indicates a level set function that indicates whether each part of the design domain where an initial structure is set corresponds to a material domain where a structure is formed, a void domain where a void is formed, or a boundary between the domains, wherein a predetermined value between a value representing the material domain and a value representing the void domain represents the boundary; and a level set function update part that, under a predetermined constraint condition, updates the level set function so as to bring a performance of the structure, such as a rigidity, close to a target value, and simultaneously varies a configuration and a shape of the boundary between the material domain and the void domain. 